domains / business / continuous-discovery / continuous-discovery

continuous-discovery

Skill card

combined

The two skills cover the same domain but use different primary mechanisms. The wondelai skill is a structured, diagnostic framework centered on Opportunity Solution Trees, experience mapping, and a 10-point scoring system. The RefoundAI skill is a qualitative, principle-based guide derived from podcast insights, focusing on removing layers between builders and users, forward-deployed engineering, and informal high-signal contact. Neither is a reproduction of the other; they offer complementary but distinct approaches to establishing a discovery cadence.

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Combined skill

# Continuous Discovery Habits

## 1. Audit and Score Current State
Evaluate the current frequency and quality of customer touchpoints. Use a diagnostic scale (e.g., 1-10) to identify gaps in the current cadence. Identify existing gatekeepers between the product team and users.

## 2. Establish the Cadence
Define a weekly rhythm for the product trio (PM, designer, engineer). This should include at least one direct customer touchpoint per week. Consider 'Exposure Hours' where team members spend time in support or onboarding to build empathy.

## 3. Map the Opportunity Space
- **Opportunity Solution Tree (OST):** Build a living tree connecting desired outcomes to customer opportunities (needs/pains) to solutions and experiments.
- **Experience Mapping:** Map the current-state customer journey to identify high-emotion moments and friction points that become opportunities.

## 4. Gather Evidence
- **Interview Snapshots:** Conduct story-based interviews and synthesize them into one-page snapshots for the team.
- **Direct Observation:** For high-value or complex problems, use 'Forward Deployed Engineering' or direct observation to perform the customer's work alongside them.
- **Informal Contact:** Maintain high-signal informal channels (e.g., direct messaging) with motivated users for real-time feedback.

## 5. Test and Iterate
Translate opportunities into lightweight experiments. Launch rough, incomplete versions to gather real-world data. Update the OST and experience maps weekly based on new evidence.